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Accelerating phylogenetics using FPGAs in the cloud

Alachiotis Nikolaos, Brokalakis Andreas, Amourgianos-Lorentzos Vasileios, Ioannidis Sotirios, Malakonakis Pavlos, Bokalidis Anastasios

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Year 2021
Type of Item Peer-Reviewed Journal Publication
Bibliographic Citation N. Alachiotis, A. Brokalakis, V. Amourgianos, S. Ioannidis, P. Malakonakis and T. Bokalidis, "Accelerating phylogenetics using FPGAs in the cloud," IEEE Micro, vol. 41, no. 4, pp. 24-30, 1 July-Aug. 2021, doi: 10.1109/MM.2021.3075848.
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Phylogenetics study the evolutionary history of organisms using an iterative process of creating and evaluating phylogenetic trees. This process is very computationally intensive; constructing a large phylogenetic tree requires hundreds to thousands of CPU hours. In this article, we describe an FPGA-based system that can be deployed on AWS EC2 F1 cloud instances to accelerate phylogenetic analyses by boosting performance of the phylogenetic likelihood function, i.e., a widely employed tree-evaluation function that accounts for up to 95% of the overall analysis time. We exploit domain-specific knowledge to reduce the amount of transferred data that limits overall system performance. Our proof-of-concept implementation reveals that the effective accelerator throughput nearly quadruples with optimized data movement, reaching up to 75% of its theoretical peak and nearly 10× faster processing than a CPU using AVX2 extensions.